AIpacman

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AIpacman offers a comprehensive Python toolkit for developing, visualizing, and benchmarking AI agents in the classic Pac-Man environment. It includes implementations of search algorithms (DFS, BFS, A*, UCS), adversarial techniques (Minimax, Alpha-Beta, Expectimax), and reinforcement learning methods (Q-Learning). With flexible maze configurations, performance metrics, and CLI controls, users can easily extend agents, analyze strategies, and gain hands-on AI experience.
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May 08 2025
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AIpacman

AIpacman

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0
AIpacman
AIpacman offers a comprehensive Python toolkit for developing, visualizing, and benchmarking AI agents in the classic Pac-Man environment. It includes implementations of search algorithms (DFS, BFS, A*, UCS), adversarial techniques (Minimax, Alpha-Beta, Expectimax), and reinforcement learning methods (Q-Learning). With flexible maze configurations, performance metrics, and CLI controls, users can easily extend agents, analyze strategies, and gain hands-on AI experience.
Added on:
Social & Email:
Platform:
May 08 2025
--
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What is AIpacman?

AIpacman is an open-source Python project that simulates the Pac-Man game environment for AI experimentation. Users can choose from built-in agents or implement custom ones using search algorithms like DFS, BFS, A*, UCS; adversarial methods such as Minimax with Alpha-Beta pruning and Expectimax; or reinforcement learning techniques like Q-Learning. The framework provides configurable mazes, performance logging, visualization of agent decision-making, and a command-line interface for running matches and comparing scores. It is designed to facilitate educational lessons, research benchmarks, and hobbyist projects in AI and game development.

Who will use AIpacman?

  • AI students and educators
  • Reinforcement learning researchers
  • Game development hobbyists
  • AI algorithm developers
  • Computer science instructors

How to use the AIpacman?

  • Step1: git clone https://github.com/YKeanoe/AIpacman.git
  • Step2: cd AIpacman && pip install -r requirements.txt
  • Step3: Open pacman.py and select or define your agent class
  • Step4: Run python pacman.py -p YourAgent -l layoutName
  • Step5: Observe game visualization and performance metrics
  • Step6: Modify agent code or maze layouts and repeat

Platform

  • mac
  • windows
  • linux

AIpacman's Core Features & Benefits

The Core Features

  • Search-based agents: DFS, BFS, UCS, A*
  • Adversarial agents: Minimax, Alpha-Beta, Expectimax
  • Reinforcement learning: Q-Learning
  • Configurable maze layouts
  • Game visualization and rendering
  • Performance logging and metrics
  • CLI-driven execution

The Benefits

  • Hands-on learning for AI algorithms
  • Easy extension with custom agents
  • Benchmark and compare strategies
  • Flexible environment configurations
  • Clear visualization of decision processes

AIpacman's Main Use Cases & Applications

  • Teaching search and game AI concepts
  • Benchmarking reinforcement learning agents
  • Researching adversarial search strategies
  • Developing custom game AI projects
  • Demonstrating AI decision-making in classrooms

FAQs of AIpacman

AIpacman Company Information

AIpacman Reviews

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AIpacman's Main Competitors and alternatives?

  • Berkeley Pacman Projects
  • OpenAI Gym Pac-Man environment
  • Python Learning Environments (PLE)
  • RLlib Game Environments
  • Malmo (Minecraft AI Testbed)

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